Cambricon’s MLU chips aren’t just processors — they’re China’s answer to scalable, efficient AI acceleration across cloud, edge, and data center environments.
This instructor-led training guides engineers and AI developers through the Cambricon stack: from deep learning model deployment to performance optimization on MLU hardware.
Courses are delivered either as online live training via interactive remote desktop, or onsite in Sofia, where hands-on labs mirror the AI challenges Cambricon is built to solve.
Whether you're scaling up an AI lab or future-proofing a data center team, onsite sessions can take place at your facility in Sofia or in a NobleProg training center designed for immersive technical learning.
Also referred to as Cambricon AI, MLU accelerator, or Machine Learning Unit, this training supports teams building AI infrastructure beyond the conventional GPU path.
NobleProg – Your Local Training Provider
Crystal Business Center
ул. "Осогово" 40, Sofia, Bulgaria, 1303
Crystal Business Center is located in the central part of Sofia, on the corner of "Osogovo" street. and "Todor Aleksandrov" blvd. The building is easily accessible by metro (only 50 m from Opalchenska station) and other public transport. Its total area is 8000 sq.m. The office area is 6171 sq.m.
Ascend, Biren, and Cambricon stand as premier AI hardware platforms in China, providing distinct acceleration and profiling solutions tailored for large-scale AI workloads in production.
This instructor-led live training (available online or onsite) targets advanced AI infrastructure and performance engineers who aim to optimize model inference and training processes across various Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
Evaluate models on Ascend, Biren, and Cambricon platforms through benchmarking.
Diagnose system bottlenecks and identify inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Refine deployment pipelines to enhance both throughput and reduce latency.
Course Format
Interactive lectures and discussions.
Practical application of profiling and optimization tools specific to each platform.
Guided exercises designed around real-world tuning scenarios.
Customization Options
For customized training tailored to your specific performance environment or model requirements, please contact us to arrange.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and HPC markets.
This instructor-led, live training session (available online or onsite) targets advanced-level GPU programmers and infrastructure specialists seeking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be equipped to:
Evaluate the compatibility of existing CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance metrics and identify optimization opportunities across different platforms.
Address practical challenges related to cross-architecture support and deployment.
Course Format
Interactive lectures and discussions.
Hands-on labs involving code translation and performance comparison.
Guided exercises focusing on multi-GPU adaptation strategies.
Customization Options
To request a customized training session tailored to your specific platform or CUDA project, please contact us to arrange it.
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize inference and training tasks in both edge computing and data center environments.
This instructor-led live training, available online or on-site, is designed for intermediate-level developers looking to build and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completing this training, participants will be able to:
Configure and set up the development environments for BANGPy and Neuware.
Develop and optimize Python- and C++-based models for Cambricon MLUs.
Deploy models to edge devices and data centers running the Neuware runtime.
Integrate ML workflows with acceleration features specific to MLU hardware.
Course Format
Interactive lectures and discussions.
Practical, hands-on exercises using BANGPy and Neuware for development and deployment.
Guided labs focusing on optimization, integration, and testing.
Customization Options
For a customized training session tailored to your specific Cambricon device model or use case, please contact us to arrange.
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